World Data Viz Challenge Barcelona-Kobe 2020
Marta Royo-Llonch
10/26/2020
library(tidyverse)
library(sf)
library(proj4)
library(spDataLarge)
library(raster)
library(osmdata)
library(hrbrthemes)
library(patchwork)
library(ggnewscale)
library(colormap)
library(reshape2)
library(rgdal)This file contains the code used for to generate the dataviz infography presentend in the World Data Viz Challenge Barcelona-Kobe 2020, named "Acoustic vulnerability of schools in Barcelona".
Barcelona streetmap
I have followd the tutorial by estebanmoro.org on plotting OSMdata in R.
Definition of the the area where we are going to query OSM:
bbx <- getbb("Barcelona")
bbx_long <- bbx %>%
melt()%>%
mutate(code=paste(Var1,Var2,sep="_"))Let's collect data from Barcelona's highways, streets and rivers:
highways <- bbx %>%
opq()%>%
add_osm_feature(key = "highway",
value=c("motorway", "trunk",
"primary","secondary",
"tertiary","motorway_link",
"trunk_link","primary_link",
"secondary_link",
"tertiary_link")) %>%
osmdata_sf()
streets <- bbx %>%
opq()%>%
add_osm_feature(key = "highway",
value = c("residential", "living_street",
"service","unclassified",
"pedestrian", "footway")) %>%
osmdata_sf()
river <- bbx%>%
opq()%>%
add_osm_feature(key = "waterway", value = "river") %>%
osmdata_sf()Neighborhood mapping
I have downloaded the official shapefiles for the city of Barcelona from martgnz's github. For convenience, I have eliminated all accents and catalan characters that could mess up the data processing. That is the reason why some input files contain the code noaccentsin their filename. Original files are also provided in the data/ .
barris <- st_read("../data/0301040100_Barris_ADM_ETRS89.shp")## Reading layer `0301040100_Barris_ADM_ETRS89' from data source `/Users/martaroyo/Dropbox/BCN_open_data_2020/github/data/0301040100_Barris_ADM_ETRS89.shp' using driver `ESRI Shapefile'
## Simple feature collection with 73 features and 46 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 420812.5 ymin: 4574282 xmax: 435480.4 ymax: 4591066
## projected CRS: ETRS89 / UTM zone 31NNow I want to merge information on districts so that coloring is made easier. I am using OpenDataBCN's file on neighborhood surface, modifying accents and catalan characters in app TextEdit:
districte <- read.csv("../data/2019_superficie_noaccents.csv")
barris.df <- barris %>%
rename(Codi_Barri=BARRI)%>%
mutate(Codi_Barri=as.numeric(Codi_Barri))%>%
left_join(districte,by="Codi_Barri")%>%
rownames_to_column(var="rowname")%>%
mutate(rowname=as.numeric(rowname))This dataframe contains all info on neighborhoods and districts plus maximum coordinates per neighborhood so that we can plot individual districts or neighborhoods in the following step:
barris.df.2 <- as.data.frame(st_coordinates(barris.df$geometry))%>%
group_by(L3)%>%
summarise(min_x=min(X),
min_y=min(Y),
max_x=max(X),
max_y=max(Y))%>%
as.data.frame()%>%
mutate(rowname=L3)%>%
left_join(barris.df,
by="rowname")Let's plot the city's districts:
dist.cols <- c("Ciutat Vella"="#3a506b",
"Eixample"="#B5E7E9",
"Gracia"="#9b90b6",
"Horta-Guinardo"="#f0a587",
"Les Corts"="#fd9b9f",
"Nou Barris"="#A6C9FC",
"Sant Andreu"="#6e65f6",
"Sant Marti"="#FDF6C0",
"Sants-Montjuic"="#64B1C6",
"Sarria-Sant Gervasi"="#d2f4b8")
ggplot()+
geom_sf(data = streets$osm_lines,
col = "#bbb4b6",
size = .1,
alpha = .5) +
geom_sf(data = highways$osm_lines,
col = "#bbb4b6",
size = .3,
alpha = .5)+
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
color = "#4adbc8",
size = 1.4,
alpha = .5) +
geom_sf(data=barris.df,
aes(fill=factor(Nom_Districte),
col=factor(Nom_Districte)),
alpha=0.8,size=0.25)+
coord_sf(xlim = c(2.052498,2.228356),
ylim = c(41.317035,41.47),
expand = FALSE)+
theme_ipsum_rc(grid = F)+
scale_fill_manual("Districte:",values=dist.cols)+
scale_color_manual("Districte:",values=dist.cols)+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x =element_blank(),
axis.title.y = element_blank())
Child's education points
Let's load data on child's education centers:
edu <- read.csv("../data/E0001_Ensenyament_Infantil_noaccents.csv")%>%
dplyr::select(CODI_EQUIPAMENT,EQUIPAMENT,NUM_BARRI,NOM_DISTRICTE,
LATITUD,LONGITUD,X_ED50,Y_ED50)
#equipments per district
n.edu.dist <- edu %>%
dplyr::select(CODI_EQUIPAMENT,NOM_DISTRICTE)%>%
distinct()%>%
group_by(NOM_DISTRICTE)%>%
summarise(equi.districte=n())Acoustic contamination
Let's load data with coustic contaminatino info:
sf <- sf::st_read("../data/2017_tramer_mapa_estrategic_soroll_bcn.gpkg", quiet = T)
df <- as.data.frame(sf::st_coordinates(sf))%>%
rownames_to_column(var="ID")
#create a simple table only with x and y coordinates to correctly transform in the following step
#we name it x and y
df.2 <- df %>%
dplyr::select(X,Y)%>%
rename(x=X,y=Y)Now we convert UTM to geographical coordinate system using proj4 and looking for correct transformation settings in epsg.io's website, tab Transform. We know that we have UTM31 ED50 as input and want WGS84 as output:
proj4string <- "+proj=utm +zone=31 +ellps=intl +towgs84=-87,-98,-121,0,0,0,0 +units=m +no_defs"
#original format: EPSG:23031 ED50 / UTM zone 31N
#final format: EPSG:4326 WGS 84
pj <- proj4::project(df.2, proj4string, inverse=TRUE)
latlon <- data.frame(lat=pj$y, lon=pj$x)%>%
rownames_to_column(var="ID")%>%
left_join(df%>%
dplyr::select(ID,L1,L2,X,Y),
by="ID")%>%
as.data.frame()We can arrange our information to be a true data.frame without geometry. We also mark those acoustic records that are higher than 60dB(A) which is the limit in A2 areas like schools, as they are areas of higher sensitivity to acoustic pollution.
acus_latlon <- latlon %>%
left_join(sf%>%
as.data.frame()%>%
rownames_to_column(var="L2")%>%
mutate(L2=as.numeric(L2))%>%
dplyr::select(-geom)%>%
distinct(),
by="L2")%>%
mutate(OVER_60=ifelse(TOTAL_D=="60 - 65 dB(A)","OVER_60_dB","UNDER_60_dB"),
OVER_60=ifelse(TOTAL_D=="65 - 70 dB(A)","OVER_60_dB",OVER_60),
OVER_60=ifelse(TOTAL_D=="70 - 75 dB(A)","OVER_60_dB",OVER_60),
OVER_60=ifelse(TOTAL_D=="75 - 80 dB(A)","OVER_60_dB",OVER_60))Now we want to plot this acoustic data by neighborhood. In order to produce cuter plots we will filter only those coordinates that fall within the neighborhood or district's polygon. To do so, I follow benmarwick's tutorial. Basically we need the polygon and points to be in the same coordinate system, then set CRS from points to be the same as polygon shapefile, merge files and filter data to be plotted.
#code to make sure only points within polygon are plotted:
#1.EDUCATION
points_edu <- st_as_sf(edu, coords = c("X_ED50", "Y_ED50"))
st_crs(points_edu) <- st_crs(barris.df) #set crs from edu points to crs of barris
edu_barris <- st_join(points_edu, barris.df) #this will be the data point df that we will filter
#2.ACOUSTICS
points_acu <- st_as_sf(acus_latlon, coords = c("X", "Y"))
st_crs(points_acu) <- st_crs(barris.df)
acu_barris <- st_join(points_acu,barris.df)
#sf objects can only be plotted properly with geom_sf, as we want other geoms
#we will take advantage that now we know to which district and neighborhood
#the acoustic trams belong and add this info to our original acus_latlon dataset
acus_latlon.2 <- acus_latlon %>%
left_join(as.data.frame(acu_barris)%>%
dplyr::select(TRAM,Codi_Districte,Codi_Barri)%>%
distinct(),
by="TRAM")%>%
left_join(districte,
by=c("Codi_Districte","Codi_Barri"))Distance between school and acoustic trams
We want to plot the acoustic samplings happening at 50m or closer to schools. First we generate dataframes with this info and then merge this info in distance.edu.acu.50:
# list to iterate over
dislist <- list()
dislist$names <- as.list(levels(factor(barris.df$Nom_Districte)))
dislist$ids <- c("dist_cv","dist_eix","dist_gra","dist_hg","dist_cor","dist_noub","dist_sand","dist_smar",
"dist_sants","dist_sarr")
plotlist_edu_acu <- list()
i <- 1
for (i in 1:length(dislist$names)){
#filter education and acoustic points that match only the specific district
points_edu <- st_as_sf(edu%>%
filter(NOM_DISTRICTE==dislist$names[[i]])%>%
droplevels()%>%
distinct(),
coords = c("X_ED50", "Y_ED50"))
points_acu <- st_as_sf(acus_latlon.2%>%
filter(Nom_Districte==dislist$names[[i]],
OVER_60=="OVER_60_dB")%>%
droplevels()%>%
distinct(),
coords = c("X", "Y"))
#make sure both datasets share crs
st_crs(points_acu) <- st_crs(points_edu)
#calculate distances (in m) between coordinates
edu.acu.dist.raw <- st_distance(points_edu,points_acu,by_element=FALSE)
edu.acu.dist <- edu.acu.dist.raw%>%
as.data.frame()
#setting colnames with acoustic tram ID
colnames(edu.acu.dist) <- paste(points_acu$TRAM,points_acu$ID,sep="_")
#binding distances to school dataframe
dist <- cbind(points_edu,edu.acu.dist)
#melt and filter shortest distance per school, <=50m
short.dist.50.edu <- dist%>%
as.data.frame()%>%
dplyr::select(-geometry)%>%
reshape2::melt(id.vars=c("CODI_EQUIPAMENT","EQUIPAMENT","NUM_BARRI",
"NOM_DISTRICTE","LATITUD","LONGITUD"))%>%
rename(TRAM_ID=variable,DISTANCE=value)%>%
filter(DISTANCE<=50)%>%
droplevels()%>%
dplyr::select(CODI_EQUIPAMENT,TRAM_ID,LATITUD,LONGITUD)%>%
separate(TRAM_ID,c("TRAM","ID"),sep="_")%>%
dplyr::select(-TRAM)%>%
left_join(acus_latlon.2,by="ID")%>%
drop_na()
#save object
assign(paste(dislist$ids[[i]],"_acu_edu",sep=""), short.dist.50.edu)
}
distance.edu.acu.50 <- rbind(dist_cor_acu_edu,
dist_cv_acu_edu,
dist_eix_acu_edu,
dist_gra_acu_edu,
dist_hg_acu_edu,
dist_noub_acu_edu,
dist_sand_acu_edu,
dist_sants_acu_edu,
dist_sarr_acu_edu,
dist_smar_acu_edu)Categorise schools by highest acoustic record in 50m or less
Now we can plot, for each vulnerable district, only those acoustic areas at 50m or closer to schools coordinates. This way we can locate those schools that are more prone to acoustic discomfort.
First define color palettes for acoustic categories:
cols.acu <- c("60 - 65 dB(A)"="#fef8c3",
"65 - 70 dB(A)"="#5ac1ad",
"70 - 75 dB(A)"="#4677a8",
"75 - 80 dB(A)"="#766cb3",
"<60 dB(A)"="#9edda4")Generate dataframe with the info of those centers that have a maximum acoustic record above limits at 50 m or closer. The maximum record of each center will be stored in the variable MAX_TOTALD_ID.
cat.distance.edu.acu.50 <- distance.edu.acu.50 %>%
mutate(MAX_TOTALD=ifelse(TOTAL_D=="60 - 65 dB(A)","1","OTHER"),
MAX_TOTALD=ifelse(TOTAL_D=="65 - 70 dB(A)","2",MAX_TOTALD),
MAX_TOTALD=ifelse(TOTAL_D=="70 - 75 dB(A)","3",MAX_TOTALD),
MAX_TOTALD=ifelse(TOTAL_D=="75 - 80 dB(A)","4",MAX_TOTALD))%>%
group_by(CODI_EQUIPAMENT)%>%
summarise(MAX_TOTALD_2=max(MAX_TOTALD))%>%
mutate(MAX_TOTALD_ID=ifelse(MAX_TOTALD_2=="1","60 - 65 dB(A)","OTHER"),
MAX_TOTALD_ID=ifelse(MAX_TOTALD_2=="2","65 - 70 dB(A)",MAX_TOTALD_ID),
MAX_TOTALD_ID=ifelse(MAX_TOTALD_2=="3","70 - 75 dB(A)",MAX_TOTALD_ID),
MAX_TOTALD_ID=ifelse(MAX_TOTALD_2=="4","75 - 80 dB(A)",MAX_TOTALD_ID),
TOTAL_D=MAX_TOTALD_ID)%>%
left_join(distance.edu.acu.50,
by=c("CODI_EQUIPAMENT","TOTAL_D"))Barplot to have of view of how many schools are subject to every acoustic level:
cat.distance.edu.acu.50 %>%
dplyr::select(CODI_EQUIPAMENT,MAX_TOTALD_ID)%>%
distinct()%>%
group_by(MAX_TOTALD_ID)%>%
summarise(n=n())%>%
rbind(c("<60 dB(A)",635-411))%>%
mutate(n=as.numeric(n),
perc=(n/635)*100)%>%
as.data.frame()%>%
ggplot()+
geom_col(aes(x=factor(MAX_TOTALD_ID,
levels=c("75 - 80 dB(A)","70 - 75 dB(A)",
"65 - 70 dB(A)","60 - 65 dB(A)","<60 dB(A)")),
y=n,
fill=MAX_TOTALD_ID,
col=MAX_TOTALD_ID),
alpha=0.8)+
geom_text(aes(x=factor(MAX_TOTALD_ID,
levels=c("75 - 80 dB(A)","70 - 75 dB(A)",
"65 - 70 dB(A)","60 - 65 dB(A)","<60 dB(A)")),
y=200, label=paste(as.character(n),"(",
round(perc,digits=1),"%)",sep="")))+
scale_fill_manual(values = cols.acu)+
scale_color_manual(values=cols.acu)+
theme_ipsum_rc(grid="")+
scale_x_discrete("")+
scale_y_continuous("")+
theme(axis.text.x = element_text(angle=90,hjust=1,vjust=0.5),
axis.text.y = element_blank())+
coord_flip()
Here we add info on neighborhoods so that we can see schools not only based on their maximum acoustic record, but also by neighborhood or district:
cat.nsch <- cat.distance.edu.acu.50 %>%
dplyr::select(Nom_Districte,Nom_Barri,CODI_EQUIPAMENT,MAX_TOTALD_ID)%>%
distinct()%>%
group_by(Nom_Districte,Nom_Barri,MAX_TOTALD_ID)%>%
summarise(nescoles=n())%>%
left_join(edu_barris%>%
as.data.frame()%>%
dplyr::select(-geometry)%>%
group_by(Nom_Districte)%>%
summarise(totalescoles=n())%>%
ungroup(),
by="Nom_Districte")%>%
mutate(percescoles=(nescoles/totalescoles)*100)
plot.cat.nsch <- cat.nsch%>%
left_join(cat.nsch%>%
group_by(Nom_Barri)%>%
summarise(barri_percescoles=sum(percescoles))%>%
ungroup(),
by="Nom_Barri")%>%
mutate(order=paste(Nom_Districte,barri_percescoles,Nom_Barri,sep="_"))%>%
distinct()
#Order Nom_Barri first by district name and then by percentage of schools (descending)
barri.levels <- plot.cat.nsch$Nom_Barri[order(plot.cat.nsch$Nom_Districte,
-plot.cat.nsch$barri_percescoles)]%>%
as.data.frame()%>%
distinct()%>%
as.list()
plot.cat.nsch$Nom_Barri <- factor(plot.cat.nsch$Nom_Barri,
levels=barri.levels$.)
#plot
plot.cat.nsch%>%
ggplot()+
geom_col(aes(x=Nom_Barri,
y=percescoles,
fill=MAX_TOTALD_ID,
col=MAX_TOTALD_ID),
alpha=0.8)+
theme_ipsum_rc(grid="Y")+
scale_x_discrete("")+
scale_y_continuous("",limits=c(-1,18))+
scale_fill_manual(values=cols.acu)+
scale_color_manual(values=cols.acu)+
new_scale_color()+
geom_point(data=plot.cat.nsch%>%
dplyr::select(Nom_Barri,Nom_Districte)%>%
distinct()%>%
as.data.frame(),
aes(x=Nom_Barri,
y=-1,
col=Nom_Districte),
size=5,shape=15,alpha=0.8)+
theme(axis.text.x = element_text(angle=90,hjust=1,vjust=0.5))+
theme(legend.position="bottom")
In order to detect those neighborhoods more in need of action against acoustic contamination close to schools, we calculate the mean number of schools that are in danger per neighborhood and focus on those that are above the mean.
lolli.cat.nsch <- plot.cat.nsch %>%
mutate(MEAN=mean(plot.cat.nsch$barri_percescoles),
DIFFMEAN=barri_percescoles-MEAN,
COL=ifelse(DIFFMEAN>0,"OVER","UNDER"))
lolli.cat.nsch %>%
ggplot()+
ggtitle("Percentatge d'escoles a prop de nivells acoustic nocius per sobre la mitjana de cada barri")+
geom_vline(xintercept = mean(plot.cat.nsch$barri_percescoles),
linetype="dotted",
size=0.25)+
geom_segment(aes(x = mean(plot.cat.nsch$barri_percescoles),
y = fct_reorder(Nom_Barri,as.numeric(DIFFMEAN),.desc = F),
xend = MEAN+DIFFMEAN,
yend = fct_reorder(Nom_Barri,as.numeric(DIFFMEAN),.desc = F)),
color = "grey50",
alpha=0.8)+
geom_point(aes(y=fct_reorder(Nom_Barri,as.numeric(DIFFMEAN),.desc = F),
x=MEAN+DIFFMEAN,col=COL,fill=COL),
size=4)+
geom_text(aes(y=fct_reorder(Nom_Barri,as.numeric(DIFFMEAN),.desc = F),
x=ifelse(MEAN+DIFFMEAN > MEAN, MEAN+DIFFMEAN + 0.6, MEAN+DIFFMEAN - 0.6),
label=paste(round(MEAN+DIFFMEAN,digits=1),"%",sep=""),
col=COL)) +
theme_ipsum_rc(grid="")+
scale_y_discrete("")+
scale_x_continuous("")+
theme(axis.text.x = element_text(angle=90,hjust=1,vjust=0.5),
legend.position = "none")
Some more plotting and number crunching:
#22 neighborhoods have schools over the mean daily dB, belonging to 10 districts
lolli.cat.nsch %>%
filter(COL=="OVER")%>%
droplevels()%>%
dplyr::select(Nom_Districte,nescoles,MAX_TOTALD_ID)%>%
group_by(Nom_Districte,MAX_TOTALD_ID)%>%
summarise(total_nescoles_tram=sum(nescoles))%>%
ungroup()%>%
left_join(lolli.cat.nsch%>%
filter(COL=="OVER")%>%
droplevels()%>%
dplyr::select(Nom_Districte,nescoles)%>%
group_by(Nom_Districte)%>%
summarise(total_nescoles=sum(nescoles))%>%
ungroup(),
by="Nom_Districte")%>%
ggplot()+
geom_col(aes(y=total_nescoles_tram,
x=fct_reorder(Nom_Districte,total_nescoles,.desc=T),
fill=MAX_TOTALD_ID),
alpha=0.8)+
theme_ipsum_rc(grid="Y")+
scale_x_discrete("")+
scale_y_continuous("Numero d'escoles")+
scale_fill_manual("",values=cols.acu)+
scale_color_manual("",values=cols.acu)+
theme(axis.text.x = element_text(angle=90,hjust=1,vjust=0.5),
legend.position="right")
vuln.distance.edu.acu.50 <- lolli.cat.nsch %>%
filter(COL=="OVER")%>%
droplevels()%>%
dplyr::select(Nom_Districte,Nom_Barri)%>%
distinct()%>%
left_join(cat.distance.edu.acu.50,
by=c("Nom_Districte","Nom_Barri"))%>%
ungroup()
## 277 vulnerable centres (above neighborhood mean)
vuln.distance.edu.acu.50%>%
dplyr::select(CODI_EQUIPAMENT)%>%
distinct()%>%
summarise(n())## # A tibble: 1 x 1
## `n()`
## <int>
## 1 277# Centres vulnerables a tots els districtes
plot.vuln.district <- vuln.distance.edu.acu.50%>%
dplyr::select(CODI_EQUIPAMENT,Nom_Districte)%>%
distinct()%>%
group_by(Nom_Districte)%>%
summarise(vuln.districte=n())%>%
ungroup()%>%
left_join(n.edu.dist%>%
rename(Nom_Districte=NOM_DISTRICTE),
by="Nom_Districte")%>%
mutate(vuln.perc=(vuln.districte/equi.districte)*100,
non.vuln.perc=(100-vuln.perc),
non.vuln.n=equi.districte-vuln.districte)
# barplot with vulnerable centers per district out of total number of centers
cols.vuln <- c("vuln.perc"="#ff897d",
"non.vuln.perc"="#818391")
#order levels of district name as done in district map (illustrator)
plot.vuln.district$Nom_Districte <- factor(plot.vuln.district$Nom_Districte,
levels=c("Sants-Montjuic","Les Corts","Ciutat Vella",
"Eixample","Sarria-Sant Gervasi",
"Gracia","Sant Marti","Horta-Guinardo",
"Sant Andreu","Nou Barris"))
plot.vuln.district %>%
melt(id.vars=c("Nom_Districte","vuln.districte","equi.districte","non.vuln.n"))%>%
ggplot()+
ggtitle("Escoles acu-vulnerables per districte",
subtitle="Respecte el total d'escoles per districte")+
geom_col(aes(y=Nom_Districte,
x=value,
fill=variable,
col=variable),
alpha=0.2)+
scale_fill_manual("",values=cols.vuln)+
scale_color_manual("",values=cols.vuln)+
geom_text(aes(x=ifelse(variable=="non.vuln.perc",6,94),
y=Nom_Districte,
label=paste(ifelse(variable=="non.vuln.perc",equi.districte,vuln.districte)," (",
round(value,digits=1),")%",sep=""),
col=variable),
size=2.5) +
theme_ipsum_rc(grid="")+
scale_y_discrete("")+
scale_x_continuous("",breaks=c(25,50,75,100))+
theme(axis.text.x = element_text(angle=90,hjust=1,vjust=0.5),
legend.position = "none")
# Number of neighborhoods with vulnerable centers
vuln.distance.edu.acu.50%>%
dplyr::select(Nom_Barri)%>%
distinct()%>%
summarise(nbarri=n())## # A tibble: 1 x 1
## nbarri
## <int>
## 1 24# number of vulnerable centers and their classification by acoustic category
vuln.distance.edu.acu.50%>%
dplyr::select(CODI_EQUIPAMENT,MAX_TOTALD_ID,MAX_TOTALD_2)%>%
distinct()%>%
group_by(MAX_TOTALD_ID)%>%
summarise(n())## # A tibble: 4 x 2
## MAX_TOTALD_ID `n()`
## <chr> <int>
## 1 60 - 65 dB(A) 94
## 2 65 - 70 dB(A) 91
## 3 70 - 75 dB(A) 88
## 4 75 - 80 dB(A) 4# Centres vulnerables a tots els districtes per categoria acoustica
plot.vuln.tram <- vuln.distance.edu.acu.50%>%
dplyr::select(CODI_EQUIPAMENT,Nom_Districte,MAX_TOTALD_ID)%>%
distinct()%>%
group_by(Nom_Districte,MAX_TOTALD_ID)%>%
summarise(n.tram=n())%>%
ungroup()%>%
left_join(plot.vuln.district%>%
dplyr::select(Nom_Districte,vuln.districte),
by="Nom_Districte")%>%
mutate(vuln.perc=(n.tram/vuln.districte)*100)
plot.vuln.tram$Nom_Districte <- factor(plot.vuln.tram$Nom_Districte,
levels=c("Sants-Montjuic","Les Corts","Ciutat Vella",
"Eixample","Sarria-Sant Gervasi",
"Gracia","Sant Marti","Horta-Guinardo",
"Sant Andreu","Nou Barris"))
plot.vuln.tram %>%
ggplot()+
ggtitle("Escoles acu-vulnerables per districte",
subtitle="Per categoria d'immisio sonora maxima")+
geom_col(aes(y=Nom_Districte,
x=vuln.perc,
fill=MAX_TOTALD_ID,
col=MAX_TOTALD_ID),
alpha=0.8)+
scale_fill_manual("",values=cols.acu)+
scale_color_manual("",values=cols.acu)+
theme_ipsum_rc(grid="")+
scale_y_discrete("")+
scale_x_continuous("",breaks=c(25,50,75,100))+
theme(axis.text.x = element_text(angle=90,hjust=1,vjust=0.5),
legend.position = "none")
Top 5 neighborhoods with schools in higher vulnerability to acoustic contamination
Now we will map those neighborhoods of interest, using stat_bin_2d to plot acoustic records and the schools that are in danger (meaning at <=50 m from the acoustic record).
Les Corts
Data:
dis.name <- c("les Corts")
plot.schools.50m <- vuln.distance.edu.acu.50 %>%
filter(Nom_Barri==dis.name)%>%
droplevels()
dis <- barris.df %>%
filter(Nom_Barri == dis.name)
min_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_x==min(min_x))%>%
dplyr::select(min_x)
min_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_y==min(min_y))%>%
dplyr::select(min_y)
max_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_x==max(max_x))%>%
dplyr::select(max_x)
max_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_y==max(max_y))%>%
dplyr::select(max_y)Total plot:
dis%>%
ggplot() +
ggtitle(dis.name)+
stat_bin2d(data=acus_latlon.2%>%
filter(TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#fef8c3",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill="#5ac1ad",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#4677a8",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="75 - 80 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#766cb3",
binwidth = c(80,80),alpha=0.6) +
geom_sf(data = streets$osm_lines,
col = "#bbb4b6",
size = .1,
alpha = .3) +
geom_sf(data = highways$osm_lines,
col = "#bbb4b6",
size = .3,
alpha = .6)+
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
color = "#4adbc8",
size = 1,
alpha = .6) +
geom_sf(data=barris.df%>%
filter(Nom_Barri==dis.name)%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
colour = "#636363",alpha=0,size=0.6)+
geom_sf(data=edu_barris%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
dplyr::select(CODI_EQUIPAMENT,MAX_TOTALD_ID)%>%
distinct()%>%
drop_na(),
by="CODI_EQUIPAMENT"),
alpha=0.8,
col="black",
size=3)+
coord_sf(xlim = c(min_x$min_x,max_x$max_x),
ylim = c(min_y$min_y,max_y$max_y))+
scale_x_continuous(limits = c(min_x$min_x,max_x$max_x))+
scale_y_continuous(limits=c(min_y$min_y,max_y$max_y))+
theme_ipsum_rc(grid=F)+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x =element_blank(),
axis.title.y = element_blank(),
legend.position = "bottom")
El Raval
Data:
dis.name <- c("el Raval")
plot.schools.50m <- vuln.distance.edu.acu.50 %>%
filter(Nom_Barri==dis.name)%>%
droplevels()
dis <- barris.df %>%
filter(Nom_Barri == dis.name)
min_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_x==min(min_x))%>%
dplyr::select(min_x)
min_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_y==min(min_y))%>%
dplyr::select(min_y)
max_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_x==max(max_x))%>%
dplyr::select(max_x)
max_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_y==max(max_y))%>%
dplyr::select(max_y)Total plot:
dis%>%
ggplot() +
ggtitle(dis.name)+
stat_bin2d(data=acus_latlon.2%>%
filter(TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#fef8c3",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill="#5ac1ad",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#4677a8",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="75 - 80 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#766cb3",
binwidth = c(80,80),alpha=0.6) +
geom_sf(data = streets$osm_lines,
col = "#bbb4b6",
size = .1,
alpha = .3) +
geom_sf(data = highways$osm_lines,
col = "#bbb4b6",
size = .3,
alpha = .6)+
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
color = "#4adbc8",
size = 1,
alpha = .6) +
geom_sf(data=barris.df%>%
filter(Nom_Barri==dis.name)%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
colour = "#636363",alpha=0,size=0.6)+
geom_sf(data=edu_barris%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
dplyr::select(CODI_EQUIPAMENT,MAX_TOTALD_ID)%>%
distinct()%>%
drop_na(),
by="CODI_EQUIPAMENT"),
alpha=0.8,
col="black",
size=3)+
coord_sf(xlim = c(min_x$min_x,max_x$max_x),
ylim = c(min_y$min_y,max_y$max_y))+
scale_x_continuous(limits = c(min_x$min_x,max_x$max_x))+
scale_y_continuous(limits=c(min_y$min_y,max_y$max_y))+
theme_ipsum_rc(grid=F)+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x =element_blank(),
axis.title.y = element_blank(),
legend.position = "bottom")
La Vila de Gracia
Data:
dis.name <- c("la Vila de Gracia")
plot.schools.50m <- vuln.distance.edu.acu.50 %>%
filter(Nom_Barri==dis.name)%>%
droplevels()
dis <- barris.df %>%
filter(Nom_Barri == dis.name)
min_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_x==min(min_x))%>%
dplyr::select(min_x)
min_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_y==min(min_y))%>%
dplyr::select(min_y)
max_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_x==max(max_x))%>%
dplyr::select(max_x)
max_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_y==max(max_y))%>%
dplyr::select(max_y)Total plot:
dis%>%
ggplot() +
ggtitle(dis.name)+
stat_bin2d(data=acus_latlon.2%>%
filter(TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#fef8c3",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill="#5ac1ad",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#4677a8",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="75 - 80 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#766cb3",
binwidth = c(80,80),alpha=0.6) +
geom_sf(data = streets$osm_lines,
col = "#bbb4b6",
size = .1,
alpha = .3) +
geom_sf(data = highways$osm_lines,
col = "#bbb4b6",
size = .3,
alpha = .6)+
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
color = "#4adbc8",
size = 1,
alpha = .6) +
geom_sf(data=barris.df%>%
filter(Nom_Barri==dis.name)%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
colour = "#636363",alpha=0,size=0.6)+
geom_sf(data=edu_barris%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
dplyr::select(CODI_EQUIPAMENT,MAX_TOTALD_ID)%>%
distinct()%>%
drop_na(),
by="CODI_EQUIPAMENT"),
alpha=0.8,
col="black",
size=3)+
coord_sf(xlim = c(min_x$min_x,max_x$max_x),
ylim = c(min_y$min_y,max_y$max_y))+
scale_x_continuous(limits = c(min_x$min_x,max_x$max_x))+
scale_y_continuous(limits=c(min_y$min_y,max_y$max_y))+
theme_ipsum_rc(grid=F)+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x =element_blank(),
axis.title.y = element_blank(),
legend.position = "bottom")
Sant Andreu
Data:
dis.name <- c("Sant Andreu")
plot.schools.50m <- vuln.distance.edu.acu.50 %>%
filter(Nom_Barri==dis.name)%>%
droplevels()
dis <- barris.df %>%
filter(Nom_Barri == dis.name)
min_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_x==min(min_x))%>%
dplyr::select(min_x)
min_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_y==min(min_y))%>%
dplyr::select(min_y)
max_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_x==max(max_x))%>%
dplyr::select(max_x)
max_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_y==max(max_y))%>%
dplyr::select(max_y)Total plot:
dis%>%
ggplot() +
ggtitle(dis.name)+
stat_bin2d(data=acus_latlon.2%>%
filter(TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#fef8c3",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill="#5ac1ad",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#4677a8",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="75 - 80 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#766cb3",
binwidth = c(80,80),alpha=0.6) +
geom_sf(data = streets$osm_lines,
col = "#bbb4b6",
size = .1,
alpha = .3) +
geom_sf(data = highways$osm_lines,
col = "#bbb4b6",
size = .3,
alpha = .6)+
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
color = "#4adbc8",
size = 1,
alpha = .6) +
geom_sf(data=barris.df%>%
filter(Nom_Barri==dis.name)%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
colour = "#636363",alpha=0,size=0.6)+
geom_sf(data=edu_barris%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
dplyr::select(CODI_EQUIPAMENT,MAX_TOTALD_ID)%>%
distinct()%>%
drop_na(),
by="CODI_EQUIPAMENT"),
alpha=0.8,
col="black",
size=3)+
coord_sf(xlim = c(min_x$min_x,max_x$max_x),
ylim = c(min_y$min_y,max_y$max_y))+
scale_x_continuous(limits = c(min_x$min_x,max_x$max_x))+
scale_y_continuous(limits=c(min_y$min_y,max_y$max_y))+
theme_ipsum_rc(grid=F)+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x =element_blank(),
axis.title.y = element_blank(),
legend.position = "bottom")
Sants
Data:
dis.name <- c("Sants")
plot.schools.50m <- vuln.distance.edu.acu.50 %>%
filter(Nom_Barri==dis.name)%>%
droplevels()
dis <- barris.df %>%
filter(Nom_Barri == dis.name)
min_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_x==min(min_x))%>%
dplyr::select(min_x)
min_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(min_y==min(min_y))%>%
dplyr::select(min_y)
max_x <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_x==max(max_x))%>%
dplyr::select(max_x)
max_y <- barris.df.2%>%
filter(Nom_Barri == dis.name)%>%
droplevels()%>%
filter(max_y==max(max_y))%>%
dplyr::select(max_y)Total plot:
dis%>%
ggplot() +
ggtitle(dis.name)+
stat_bin2d(data=acus_latlon.2%>%
filter(TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="60 - 65 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#fef8c3",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="65 - 70 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill="#5ac1ad",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="70 - 75 dB(A)")%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#4677a8",
binwidth = c(80,80),alpha=0.6) +
stat_bin2d(data=acus_latlon.2%>%
filter(Nom_Barri==dis.name,
TOTAL_D=="75 - 80 dB(A)")%>%
droplevels()%>%
drop_na()%>%
distinct()%>%
right_join(vuln.distance.edu.acu.50%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
aes(x=X,y=Y),
fill = "#766cb3",
binwidth = c(80,80),alpha=0.6) +
geom_sf(data = streets$osm_lines,
col = "#bbb4b6",
size = .1,
alpha = .3) +
geom_sf(data = highways$osm_lines,
col = "#bbb4b6",
size = .3,
alpha = .6)+
geom_sf(data = river$osm_lines,
inherit.aes = FALSE,
color = "#4adbc8",
size = 1,
alpha = .6) +
geom_sf(data=barris.df%>%
filter(Nom_Barri==dis.name)%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
droplevels()%>%
dplyr::select(Nom_Barri)%>%
distinct(),
by="Nom_Barri"),
colour = "#636363",alpha=0,size=0.6)+
geom_sf(data=edu_barris%>%
right_join(vuln.distance.edu.acu.50%>%
filter(Nom_Barri==dis.name)%>%
dplyr::select(CODI_EQUIPAMENT,MAX_TOTALD_ID)%>%
distinct()%>%
drop_na(),
by="CODI_EQUIPAMENT"),
alpha=0.8,
col="black",
size=3)+
coord_sf(xlim = c(min_x$min_x,max_x$max_x),
ylim = c(min_y$min_y,max_y$max_y))+
scale_x_continuous(limits = c(min_x$min_x,max_x$max_x))+
scale_y_continuous(limits=c(min_y$min_y,max_y$max_y))+
theme_ipsum_rc(grid=F)+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x =element_blank(),
axis.title.y = element_blank(),
legend.position = "bottom")
That's all! After generating the figures I have used Illustrator CC 2018 to generate the infographic.